Abstract

I will describe the votlage, megawatts, megavars (receieved and delivered), and power factor (lagging and leading) in terms of the electricity. Key goals are to understand substations and meters with regards to voltage and power factor as well as megawatts and megavars.

Introduction

Electric utilities collect meter readings in time intervals in various units. The intervals can be from 1 minute to 60 minute intervals collecing kW, kWh, VARh, volts. An utility has to maintain a specified range for voltage across their electric network. When voltage is too low brown outs or electric motors may fail to work and when voltage is too high appliances and equiment can overheat, burn up, and possibly explode.

Power Factor

A negative power factor (Lagging) occurs when the device (which is normally the load) generates power, which then flows back towards the source, which is normally considered the generator.

In an electric power system, a load with a low power factor draws more current than a load with a high power factor for the same amount of useful power transferred.

The higher currents increase the energy lost in the distribution system, and require larger wires and other equipment. Because of the costs of larger equipment and wasted energy, electrical utilities will usually charge a higher cost to industrial or commercial customers where there is a low power factor.

Power factors below 1.0 require a utility to generate more than the minimum volt-amperes necessary to supply the real power (watts).
This increases generation and transmission costs. For example, if the load power factor were as low as 0.7, the apparent power would be 1.4 times the real power used by the load. Line current in the circuit would also be 1.4 times the current required at 1.0 power factor, so the losses in the circuit would be doubled (since they are proportional to the square of the current).

Alternatively all components of the system such as generators, conductors, transformers, and switchgear would be increased in size (and cost) to carry the extra current.

Utilities typically charge additional costs to commercial customers who have a power factor below some limit, which is typically 0.9 to 0.95. Engineers are often interested in the power factor of a load as one of the factors that affect the efficiency of power transmission.

More Definitions

Since the units are consistent, the power factor is by definition a dimensionless number between −1 and 1. When power factor is equal to 0, the energy flow is entirely reactive and stored energy in the load returns to the source on each cycle. When the power factor is 1, all the energy supplied by the source is consumed by the load.

Power factors are usually stated as “leading” or “lagging” to show the sign of the phase angle. Capacitive loads are leading (current leads voltage) and supply power, and inductive loads are lagging (current lags voltage) and consume power.

Structure of Voltage Data

## 'data.frame':    3033369 obs. of  4 variables:
##  $ readdate      : Factor w/ 1056 levels "2016-02-28 00:15:00",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ Voltage       : num  235 237 237 236 236 ...
##  $ substationName: Factor w/ 24 levels "Boynton Valley",..: 23 23 23 23 23 23 23 23 23 23 ...
##  $ meter         : int  300063 300063 300063 300063 300063 300063 300063 300063 300063 300063 ...

Structure of Power Factor Data

## 'data.frame':    10800 obs. of  4 variables:
##  $ ReadValue: num  0 0 0 0 0 0 0 0 0 0 ...
##  $ station  : Factor w/ 15 levels "BOYN","CHPH",..: 4 4 4 4 4 4 4 4 4 4 ...
##  $ name     : Factor w/ 3 levels "PMQD3D","PMQD3R",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ ReadDate : Factor w/ 240 levels "2016-02-28 00:10:00.000",..: 1 2 3 4 5 6 235 236 237 238 ...

Univariate Plots Section

Voltage

Range of days: 2016-02-28, 2016-03-09

Summary of Voltage: 100.6, 120.8, 122.4, 122, 123.4, 130

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   100.6   120.8   122.4   122.0   123.4   130.0

Power Factor

MegaWatts, MVARs, and MVARS Squared

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   5.832   7.704   8.962  10.840  25.240

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -3.572  -1.274  -0.754  -0.237   0.752   7.080     240

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.567   1.210   3.040   2.760  50.130     240

Univariate Analysis

What is the structure of your dataset?

Power Factor

The power factor dataset contains 10,800 obs. of 4 variables which are readings taken every 60 minutes.
* ReadValue: num
* station : 4 Charcter Desc of substation in this system (SCADA System)
* name : Unit of Measure
* mega watt
* mvars delievered
* mvars received
* ReadDate : string in yyyy-mm-dd hh:mm:ss format

Voltage

The power factor dataset contains 3,033,369 obs. of 4 variables which are readings taken every 15 minutes. * readdate: string in yyyy-mm-dd hh:mm:ss format
* Voltage : integer
* substationName: string of full substation name
* meter : integer

The readings for each meter occur every 15 minutes.

Substations

  • station : 4 Charcter Desc of substation in this system (SCADA System)
  • substationName: string of full substation name
  • prettyName: string of full substation name

Tidy Datasets

Voltage Dataset

## Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame':  3031348 obs. of  10 variables:
##  $ substationName: chr  "CORNERSVILLE" "BOYNTON VALLEY" "CORNERSVILLE" "WARTRACE" ...
##  $ meter         : int  200754 300615 200045 202095 301042 301144 300434 200372 301639 200728 ...
##  $ readdate      : Factor w/ 1056 levels "2016-02-28 00:15:00",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ dtReadDate    : POSIXct, format: "2016-02-28" "2016-02-28" ...
##  $ dtReadDay     : POSIXct, format: "2016-02-28" "2016-02-28" ...
##  $ h             : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ hm            : chr  "00:15" "00:15" "00:15" "00:15" ...
##  $ Voltage       : num  226 230 230 230 230 ...
##  $ VoltsHalf     : num  113 115 115 115 115 ...
##  $ voltage.bucket: Factor w/ 4 levels "(0,114]","(114,120]",..: 1 2 2 2 2 2 2 2 2 2 ...
##  - attr(*, "vars")=List of 4
##   ..$ : symbol readdate
##   ..$ : symbol Voltage
##   ..$ : symbol substationName
##   ..$ : symbol meter
##  - attr(*, "drop")= logi TRUE
##  - attr(*, "indices")=List of 3031348
##   ..$ : int 0
##   ..$ : int 1
##   ..$ : int 2
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##   .. [list output truncated]
##  - attr(*, "group_sizes")= int  1 1 1 1 1 1 1 1 1 1 ...
##  - attr(*, "biggest_group_size")= int 1
##  - attr(*, "labels")='data.frame':   3031348 obs. of  4 variables:
##   ..$ readdate      : Factor w/ 1056 levels "2016-02-28 00:15:00",..: 1 1 1 1 1 1 1 1 1 1 ...
##   ..$ Voltage       : num  226 230 230 230 230 ...
##   ..$ substationName: chr  "CORNERSVILLE" "BOYNTON VALLEY" "CORNERSVILLE" "WARTRACE" ...
##   ..$ meter         : int  200754 300615 200045 202095 301042 301144 300434 200372 301639 200728 ...
##   ..- attr(*, "vars")=List of 4
##   .. ..$ : symbol readdate
##   .. ..$ : symbol Voltage
##   .. ..$ : symbol substationName
##   .. ..$ : symbol meter
##   ..- attr(*, "drop")= logi TRUE
##   ..- attr(*, "indices")=List of 3031348
##   .. ..$ : int 110886
##   .. ..$ : int 80986
##   .. ..$ : int 1153179
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##   .. .. [list output truncated]
##   ..- attr(*, "group_sizes")= int  1 1 1 1 1 1 1 1 1 1 ...
##   ..- attr(*, "biggest_group_size")= int 1

Power Factor Dataset

## Classes 'tbl_df', 'tbl' and 'data.frame':    3600 obs. of  15 variables:
##  $ substationName: chr  "BOYNTON VALLEY" "BOYNTON VALLEY" "BOYNTON VALLEY" "BOYNTON VALLEY" ...
##  $ ReadDate      : Factor w/ 240 levels "2016-02-28 00:10:00.000",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ dtReadDate    : POSIXct, format: "2016-02-28 00:00:00" "2016-02-28 01:00:00" ...
##  $ dtReadDay     : POSIXct, format: "2016-02-28" "2016-02-28" ...
##  $ h             : num  0 1 2 3 4 5 6 7 8 9 ...
##  $ hm            : chr  "00:15" "01:15" "02:15" "03:15" ...
##  $ mw            : num  9.33 9.34 9.36 9.55 9.81 ...
##  $ mvar.delivered: num  0 0 0 0 0 0 0 0 0 0 ...
##  $ mvar.received : num  0.84 0.771 0.742 0.712 0.675 ...
##  $ mvar          : num  -0.84 -0.771 -0.742 -0.712 -0.675 ...
##  $ mwsquared     : num  87.1 87.2 87.7 91.3 96.3 ...
##  $ mvarsquared   : num  0.706 0.594 0.551 0.507 0.456 ...
##  $ pf            : num  0.996 0.997 0.997 0.997 0.998 ...
##  $ pfChart       : num  1 1 1 1 1 ...
##  $ desc          : Factor w/ 2 levels "Lagging","Leading": 2 2 2 2 2 2 2 2 2 2 ...

Substation Dataset

## 'data.frame':    27 obs. of  3 variables:
##  $ station       : Factor w/ 27 levels "BOYN","CHPH",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ substationName: Factor w/ 27 levels "BOYNTON VALLEY",..: 1 2 3 4 5 6 25 7 8 9 ...
##  $ prettyName    : Factor w/ 27 levels "Boynton Valley",..: 1 2 3 4 5 6 25 7 8 9 ...

What is/are the main feature(s) of interest in your dataset?

Voltage and Power Factor by time interval for each deliver point (substation) are the main features. One goal is to determine if we can predict power factor.

What other features in the dataset do you think will help support your

investigation into your feature(s) of interest? * mega watt
* mega var delivered
* mega var received

Did you create any new variables from existing variables in the dataset?

I created the power factor, mvars and the direction of the power factor (lagging and leading).

New variables where created for these datasets:

powerfactor_tidy and voltage_tidy

  • dtReadDate
  • dtReadDay
  • h
  • hm

voltage_tidy

  • voltage.bucket
  • VoltsHalf

powerfactor_tidy

  • mvars
  • mvarsquared
  • Power Factor
  • Power Factor Chart (Shifted Value)
  • desc - the direction of the power factor (Lagging vs Leading)

Of the features you investigated, were there any unusual distributions?

Williamsport and Mt Pleasent have bimodel distibutions of voltage.

The long leading tail on the voltage histogram, has a larger range in the data in the lower range than in the upper range.

Number of voltage intervals < 117: 43458
The range is : 100.6, 116.95

Number of voltage intervals > 126: 41439
The range is : 126.05, 129.95

Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?

Various methods were used to clean the data. For instance the ReadDates for the voltage intervals are in ending interval. The interval starts at 3/2/2016 00:15min and ends on 3/3/2016 00:00. To associate the 15 minute intervals with the correct hour and day, we had to roll back each 15 minute interval by 15 minutes.

The power factor data needed to be pivoted to get the data into a tidy format as well. The orignal data has the MegaWatt, MegaVars Delivered and Received in the same column, these values were split out into their own columns.

The substation names can have leading and trailing spaces so this data needed to be trimed.

Bivariate Plots Section

Voltage Plots

Box Plots

Scatter Plots

The scatter plot shows the data along the time axis for the intervals for the day. The interesting point in this chart, which is similar to the histogram is how the shading changes from dark to grey, which is the points stacking on top of each other.

It takes 5 points on top of each other to make a solid point on this chart.
This demonstrates how the data is spread out over the ranges through the day by hour.

Voltage Descriptive Statistics
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   100.6   120.8   122.4   122.0   123.4   130.0
Standard Deviation
## [1] 2.047819
Range
## [1] 100.60 129.95
Quantiles
##     0%    25%    50%    75%   100% 
## 100.60 120.80 122.35 123.45 129.95

Mega Watts

Mega Watts Descriptive Statistics
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   5.832   7.704   8.962  10.840  25.240

MegaVAR

MegaVAR Descriptive Statistics
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -3.572  -1.274  -0.754  -0.237   0.752   7.080     240

System Total MegaWatts, Mega VARs, MegaVars Squared

Power Factor Plots

Box Plots

Scatter Plots

Voltage.Min vs [MW, MVAR]

Voltage.Mean vs [MW, MVAR, Power Factor]

Voltage [Min, Mean, Median, Max] vs MW

Additional Substation Analysis

Substations meters where the voltage is less than nominal

Voltage Low Limit

Meters where more than 4 intervals below the 114 volts threshold.

Sample of Meters

Top Top 10 Substations Meter Counts for Low Voltage Meters

substationName meter dtReadDay count
CORNERSVILLE 301146 2016-03-03 35
BOYNTON VALLEY 200227 2016-03-03 31
CORNERSVILLE 301146 2016-02-28 28
CORNERSVILLE 300482 2016-03-03 27
UNIONVILLE 300964 2016-03-03 27
CORNERSVILLE 301146 2016-03-01 23
CORNERSVILLE 301146 2016-03-07 23
CORNERSVILLE 300482 2016-03-01 21
FOUNDRY HILL 301042 2016-03-05 21
CORNERSVILLE 301089 2016-02-28 20

Voltage High Limit

Meters where more than 4 intervals below the 126 volts threshold.

Top Top 10 Substations Meter Counts for High Voltage Meters

substationName meter dtReadDay count
BOYNTON VALLEY 200037 2016-03-02 1
BOYNTON VALLEY 200037 2016-03-08 1
BOYNTON VALLEY 200037 2016-03-09 1
BOYNTON VALLEY 200037 2016-03-06 3
BOYNTON VALLEY 200037 2016-02-29 7
BOYNTON VALLEY 200038 2016-03-02 1
BOYNTON VALLEY 200038 2016-03-08 1
BOYNTON VALLEY 200038 2016-03-09 1
BOYNTON VALLEY 200038 2016-03-06 5
BOYNTON VALLEY 200038 2016-02-29 10

Bivariate Analysis

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?

Features of Interest

  • Voltage
  • By Substation

  • By Hour

  • Power Factor
  • By Substation

  • By Hour

Other Features

  • MW
  • MVAR

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?

This shows an interesting trend starting at 11AM (11:00 hours) until 10PM (20:00 hrs). The power factor spreads over a wider range. This is interesting on a system wide review, however we are more concerned with the power factor for each delivery point.

What was the strongest relationship you found?

The strongest relationship I found is between megawatts and power factor.
As the megawatts increases the power factor approaches the 1, for each of the Substations for this single day investigation.

Multivariate Plots Section

Correlation Analysis

Coefficient, r

Strength of Association Positive Negative
Small .1 to .3 -0.1 to -0.3
Medium .3 to .5 -0.3 to -0.5
Large .5 to 1.0 -0.5 to -1.0

Correlation Matrix

Review the correlation between power factor and voltage using Pearsons.

Correlation Plots Power Factor

Correlation Plots Power Factor Shifted Value

Correlation of Voltage & Power Factor

Review the correlation between power factor and voltage using Pearsons.

## 
##  Pearson's product-moment correlation
## 
## data:  summary_total$v.mean and summary_total$pf
## t = -3.7994, df = 3104, p-value = 0.0001478
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.10296188 -0.03294618
## sample estimates:
##        cor 
## -0.0680378

Voltage vs PF with Linear Model

MegaWatt vs PF with Linear Model by Substation

Power Factor Shifted Value (Lagging and Leading)

Power Factor Ratio Plot

Voltage Scatter Plots With Bucket (Cut)

Voltage Bucket Summary

Counts of the voltage instances in each bucketed range.

##   (0,114] (114,120] (120,126] (126,140] 
##      1958    520655   2467296     41439
Voltage Bucket Data
substationName (0,114] (114,120] (120,126] (126,140]
BOYNTON VALLEY 301 88157 124288 757
CCJIP NA 1902 51569 NA
CHAPEL HILL 33 10144 174030 910
COLUMBIA PRIMARY NA 2126 22387 772
CORNERSVILLE 729 119109 165467 NA
COWAN NA 68 17912 29224
CULLEOKA 10 11834 74330 NA
DECHERD 41 5333 189046 283
EAST SHELBYVILLE 153 12513 102192 NA
ESTILL SPRINGS 19 6722 108056 30
FOUNDRY HILL 114 48420 395732 94
HILLSBORO 55 19766 157340 22
KS PHILLIPS NA 1472 81805 41
LYNCHBURG NA 2243 92117 1089
MANCHESTER 161 92 18374 204109 692
MT PLEASANT 2 1659 13115 1961
RALLY HILL 49 11799 88399 NA
RED HILL NA 967 40210 NA
SALEM 2 1875 28064 25
SEWANEE 10 7568 19394 NA
SPRING HILL 1 475 88591 2
UNIONVILLE 185 62565 85819 NA
WARTRACE 155 82129 113007 6
WILLIAMSPORT 7 3435 30317 5531

This chart demonstrates how the voltage can vary on the lower and upper ends of the voltage ranges.

Multivariate Analysis

Talk about some of the relationships you observed in this part of the

investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?

  • MegaWatt vs Power Factor (GOOD)
  • Voltage vs Power Factor (NOTHING)
  • Voltage vs PF with Linear Model - Facet Substation (NOTHING)
  • MegaWatt vs PF with Linear Model by Substation (GOOD)
  • Power Factor Shifted Value (Lagging and Leading) By Hour (GOOD), FINAL CHART
  • Power Factor Ratio Plot (GOOD)

Were there any interesting or surprising interactions between features?

Voltage Scatter Plots With Bucket and Power Factor

The voltage and power factor for the entire system seem to track or follow a similar trend here. They have similar shapes. Or at least when the power factor range increases the voltage drops less in the system. This could be for many reasons.

Models

Predict Power Factor… is the goal ### OPTIONAL: Did you create any models with your dataset? Discuss the strengths and limitations of your model.

Final Plots and Summary

Are the final three plots varied and do they meet some of the following criteria:
1 Draw comparisons.
2 Identify trends.
3 Engage a wide audience.
4 Explain a complicated finding.
5 Clarify a gap between perception and reality.
6 Enable the reader to digest large amounts of information.

Each plot reveals an important and different comparison or trend in the data. The plots incorporate many of the variables from the data set in a way that allows the plots to convey a lot of information while still being interpreted easily. The plots fulfill 4 or more of the criteria.

Plot One

Voltage and Power Factor Outlier plot helps the user to quickly identify which substations are out the bounded region.

dtReadDay substationName v.min v.median v.min v.max pfc.min pfc.median pfc.max desc
2016-02-28 BOYNTON VALLEY 108.60 119.40 108.60 127.80 1.00 1.02 1.07 Leading
2016-02-29 BOYNTON VALLEY 112.10 124.05 112.10 129.00 NaN NA NaN Leading
2016-03-01 BOYNTON VALLEY 107.65 118.85 107.65 127.85 1.01 1.02 1.08 Leading
2016-03-02 BOYNTON VALLEY 111.85 122.79 111.85 128.60 1.00 1.00 1.04 Leading
2016-03-03 BOYNTON VALLEY 108.15 119.45 108.15 127.95 1.00 1.01 1.01 Leading
2016-03-04 BOYNTON VALLEY 111.70 122.92 111.70 127.80 1.00 1.01 1.03 Leading
2016-03-05 BOYNTON VALLEY 110.45 119.10 110.45 127.20 1.00 1.01 1.06 Leading
2016-03-06 BOYNTON VALLEY 113.45 123.21 113.45 129.15 1.00 1.02 1.07 Leading
2016-03-07 BOYNTON VALLEY 106.40 119.22 106.40 126.70 1.01 1.03 1.10 Leading
2016-03-08 BOYNTON VALLEY 113.35 123.60 113.35 128.70 1.02 1.04 1.11 Leading
2016-03-09 BOYNTON VALLEY 105.70 119.20 105.70 128.90 NA NA NA NA
2016-02-28 CCJIP 116.55 122.75 116.55 125.45 NA NA NA NA
2016-02-29 CCJIP 117.35 122.49 117.35 124.65 NA NA NA NA
2016-03-01 CCJIP 117.00 122.30 117.00 124.85 NA NA NA NA
2016-03-02 CCJIP 116.60 121.94 116.60 125.40 NA NA NA NA
2016-03-03 CCJIP 116.25 122.70 116.25 125.55 NA NA NA NA
2016-03-04 CCJIP 116.00 121.93 116.00 125.35 NA NA NA NA
2016-03-05 CCJIP 116.75 122.19 116.75 125.50 NA NA NA NA
2016-03-06 CCJIP 117.55 122.42 117.55 125.30 NA NA NA NA
2016-03-07 CCJIP 115.75 122.70 115.75 125.25 NA NA NA NA
2016-03-08 CCJIP 117.45 122.90 117.45 125.20 NA NA NA NA
2016-03-09 CCJIP 116.15 122.70 116.15 125.10 NA NA NA NA
2016-02-28 CHAPEL HILL 114.95 122.88 114.95 128.20 0.99 0.99 1.00 Lagging
2016-02-29 CHAPEL HILL 115.55 122.55 115.55 127.85 0.99 0.99 1.00 Lagging
2016-03-01 CHAPEL HILL 113.05 122.80 113.05 127.70 0.99 0.99 1.00 Lagging
2016-03-02 CHAPEL HILL 108.95 122.20 108.95 127.70 0.99 0.99 1.00 Lagging
2016-03-03 CHAPEL HILL 110.30 122.75 110.30 128.10 0.99 0.99 0.99 Lagging
2016-03-04 CHAPEL HILL 113.35 121.96 113.35 127.70 0.99 0.99 0.99 Lagging
2016-03-05 CHAPEL HILL 114.55 122.79 114.55 128.50 0.99 0.99 0.99 Lagging
2016-03-06 CHAPEL HILL 114.70 122.20 114.70 128.25 0.99 0.99 1.00 Lagging
2016-03-07 CHAPEL HILL 114.45 123.12 114.45 128.00 0.99 0.99 0.99 Lagging
2016-03-08 CHAPEL HILL 109.70 122.91 109.70 127.85 0.99 0.99 1.00 Lagging
2016-03-09 CHAPEL HILL 110.75 122.70 110.75 127.45 NA NA NA NA
2016-02-28 COLUMBIA PRIMARY 116.00 123.42 116.00 126.60 NA NA NA NA
2016-02-29 COLUMBIA PRIMARY 117.45 123.96 117.45 126.55 NA NA NA NA
2016-03-01 COLUMBIA PRIMARY 116.95 122.92 116.95 126.25 NA NA NA NA
2016-03-02 COLUMBIA PRIMARY 115.65 122.25 115.65 126.10 NA NA NA NA
2016-03-03 COLUMBIA PRIMARY 116.80 122.20 116.80 125.75 NA NA NA NA
2016-03-04 COLUMBIA PRIMARY 115.25 122.44 115.25 126.90 NA NA NA NA
2016-03-05 COLUMBIA PRIMARY 116.20 123.10 116.20 126.65 NA NA NA NA
2016-03-06 COLUMBIA PRIMARY 115.80 122.86 115.80 126.10 NA NA NA NA
2016-03-07 COLUMBIA PRIMARY 117.30 124.09 117.30 127.30 NA NA NA NA
2016-03-08 COLUMBIA PRIMARY 118.25 124.39 118.25 127.50 NA NA NA NA
2016-03-09 COLUMBIA PRIMARY 118.85 124.07 118.85 127.85 NA NA NA NA
2016-02-28 CORNERSVILLE 103.00 119.78 103.00 124.00 NA NA NA NA
2016-02-29 CORNERSVILLE 105.15 122.65 105.15 125.15 NA NA NA NA
2016-03-01 CORNERSVILLE 108.75 118.88 108.75 122.95 NA NA NA NA
2016-03-02 CORNERSVILLE 112.95 122.35 112.95 125.60 NA NA NA NA
2016-03-03 CORNERSVILLE 104.15 119.38 104.15 124.05 NA NA NA NA
2016-03-04 CORNERSVILLE 106.10 122.20 106.10 125.10 NA NA NA NA
2016-03-05 CORNERSVILLE 102.65 119.75 102.65 124.40 NA NA NA NA
2016-03-06 CORNERSVILLE 112.10 122.32 112.10 125.45 NA NA NA NA
2016-03-07 CORNERSVILLE 110.20 119.25 110.20 122.55 NA NA NA NA
2016-03-08 CORNERSVILLE 113.75 122.99 113.75 125.40 NA NA NA NA
2016-03-09 CORNERSVILLE 107.25 118.99 107.25 121.95 NA NA NA NA
2016-02-28 COWAN 119.60 126.46 119.60 129.50 NA NA NA NA
2016-02-29 COWAN 118.85 126.82 118.85 128.80 NA NA NA NA
2016-03-01 COWAN 119.05 126.47 119.05 128.45 NA NA NA NA
2016-03-02 COWAN 119.00 125.54 119.00 128.65 NA NA NA NA
2016-03-03 COWAN 119.25 125.79 119.25 128.85 NA NA NA NA
2016-03-04 COWAN 119.25 126.05 119.25 129.35 NA NA NA NA
2016-03-05 COWAN 118.00 126.67 118.00 129.90 NA NA NA NA
2016-03-06 COWAN 119.85 126.59 119.85 129.95 NA NA NA NA
2016-03-07 COWAN 119.45 127.18 119.45 129.55 NA NA NA NA
2016-03-08 COWAN 120.65 127.28 120.65 129.40 NA NA NA NA
2016-03-09 COWAN 122.60 127.41 122.60 129.40 NA NA NA NA
2016-02-28 CULLEOKA 114.85 121.89 114.85 125.60 1.00 1.02 1.07 Leading
2016-02-29 CULLEOKA 115.85 121.78 115.85 124.40 1.01 1.03 1.07 Leading
2016-03-01 CULLEOKA 114.70 121.90 114.70 125.40 NaN NA NaN Leading
2016-03-02 CULLEOKA 112.15 120.85 112.15 125.40 1.00 1.00 1.04 Leading
2016-03-03 CULLEOKA 114.00 122.06 114.00 125.55 1.00 1.00 1.01 Leading
2016-03-04 CULLEOKA 113.40 122.11 113.40 125.35 1.00 1.01 1.03 Leading
2016-03-05 CULLEOKA 114.20 121.89 114.20 125.55 1.00 1.01 1.07 Leading
2016-03-06 CULLEOKA 114.55 121.68 114.55 125.60 1.00 1.02 1.07 Leading
2016-03-07 CULLEOKA 114.05 122.12 114.05 125.25 1.01 1.02 1.08 Leading
2016-03-08 CULLEOKA 114.95 122.78 114.95 125.45 1.02 1.03 1.07 Leading
2016-03-09 CULLEOKA 115.25 123.00 115.25 125.70 NA NA NA NA
2016-02-28 DECHERD 115.95 122.80 115.95 126.45 NA NA NA NA
2016-02-29 DECHERD 101.35 122.92 101.35 125.95 NA NA NA NA
2016-03-01 DECHERD 115.55 122.60 115.55 126.05 NA NA NA NA
2016-03-02 DECHERD 113.40 122.75 113.40 126.75 NA NA NA NA
2016-03-03 DECHERD 114.25 123.40 114.25 126.75 NA NA NA NA
2016-03-04 DECHERD 100.80 122.97 100.80 126.45 NA NA NA NA
2016-03-05 DECHERD 115.10 122.95 115.10 127.00 NA NA NA NA
2016-03-06 DECHERD 114.90 122.40 114.90 126.65 NA NA NA NA
2016-03-07 DECHERD 102.85 122.91 102.85 125.95 NA NA NA NA
2016-03-08 DECHERD 101.25 123.28 101.25 126.20 NA NA NA NA
2016-03-09 DECHERD 100.60 123.70 100.60 126.20 NA NA NA NA
2016-02-28 EAST SHELBYVILLE 107.20 122.40 107.20 125.60 NA NA NA NA
2016-02-29 EAST SHELBYVILLE 106.90 123.00 106.90 125.40 NA NA NA NA
2016-03-01 EAST SHELBYVILLE 107.65 122.05 107.65 125.30 NA NA NA NA
2016-03-02 EAST SHELBYVILLE 107.25 121.50 107.25 125.55 NA NA NA NA
2016-03-03 EAST SHELBYVILLE 106.80 121.93 106.80 125.65 NA NA NA NA
2016-03-04 EAST SHELBYVILLE 106.35 121.67 106.35 125.45 NA NA NA NA
2016-03-05 EAST SHELBYVILLE 106.55 122.32 106.55 125.60 NA NA NA NA
2016-03-06 EAST SHELBYVILLE 105.85 121.86 105.85 125.50 NA NA NA NA
2016-03-07 EAST SHELBYVILLE 107.40 122.32 107.40 125.55 NA NA NA NA
2016-03-08 EAST SHELBYVILLE 107.65 122.83 107.65 125.85 NA NA NA NA
2016-03-09 EAST SHELBYVILLE 110.00 122.96 110.00 125.25 NA NA NA NA
2016-02-28 ESTILL SPRINGS 112.80 122.80 112.80 126.15 0.99 1.03 1.12 Leading
2016-02-29 ESTILL SPRINGS 115.70 123.09 115.70 125.85 0.99 1.04 1.08 Leading
2016-03-01 ESTILL SPRINGS 110.50 122.34 110.50 125.65 NaN NA NaN Leading
2016-03-02 ESTILL SPRINGS 113.15 122.22 113.15 126.40 0.99 1.01 1.05 Leading
2016-03-03 ESTILL SPRINGS 114.35 122.72 114.35 126.10 0.98 1.01 1.01 Leading
2016-03-04 ESTILL SPRINGS 112.75 122.46 112.75 125.90 0.99 1.02 1.04 Leading
2016-03-05 ESTILL SPRINGS 113.50 122.54 113.50 126.10 0.99 1.04 1.14 Leading
2016-03-06 ESTILL SPRINGS 112.90 122.21 112.90 125.95 0.99 1.04 1.11 Leading
2016-03-07 ESTILL SPRINGS 114.05 122.78 114.05 125.55 0.99 1.04 1.09 Leading
2016-03-08 ESTILL SPRINGS 114.95 123.49 114.95 125.75 0.99 1.07 1.12 Leading
2016-03-09 ESTILL SPRINGS 116.35 123.57 116.35 125.55 NA NA NA NA
2016-02-28 FOUNDRY HILL 112.25 122.75 112.25 126.25 1.00 1.02 1.08 Leading
2016-02-29 FOUNDRY HILL 113.75 122.90 113.75 125.60 1.01 1.04 1.11 Leading
2016-03-01 FOUNDRY HILL 113.15 122.75 113.15 125.65 NaN NA NaN Leading
2016-03-02 FOUNDRY HILL 109.70 122.50 109.70 126.75 1.00 1.01 1.06 Leading
2016-03-03 FOUNDRY HILL 106.55 122.35 106.55 125.95 NaN NA NaN Leading
2016-03-04 FOUNDRY HILL 112.95 122.25 112.95 125.80 1.00 1.01 1.05 Leading
2016-03-05 FOUNDRY HILL 109.60 122.72 109.60 126.45 1.00 1.02 1.08 Leading
2016-03-06 FOUNDRY HILL 110.95 122.50 110.95 126.10 1.00 1.02 1.08 Leading
2016-03-07 FOUNDRY HILL 110.55 122.50 110.55 125.55 NaN NA NaN Leading
2016-03-08 FOUNDRY HILL 110.00 123.10 110.00 125.60 1.01 1.04 1.12 Leading
2016-03-09 FOUNDRY HILL 113.45 122.97 113.45 125.45 NA NA NA NA
2016-02-28 HILLSBORO 114.05 122.33 114.05 126.20 0.99 1.02 1.06 Leading
2016-02-29 HILLSBORO 112.10 122.40 112.10 125.90 0.99 1.03 1.08 Leading
2016-03-01 HILLSBORO 111.40 121.78 111.40 125.65 NaN NA NaN Leading
2016-03-02 HILLSBORO 109.90 121.75 109.90 126.05 0.99 1.00 1.03 Leading
2016-03-03 HILLSBORO 112.00 122.10 112.00 126.00 0.98 1.00 1.01 Leading
2016-03-04 HILLSBORO 110.10 122.00 110.10 126.05 0.99 1.01 1.03 Leading
2016-03-05 HILLSBORO 113.30 122.07 113.30 126.20 0.99 1.02 1.05 Leading
2016-03-06 HILLSBORO 108.65 121.80 108.65 126.25 0.99 1.02 1.06 Leading
2016-03-07 HILLSBORO 113.50 122.50 113.50 125.40 0.99 1.03 1.07 Leading
2016-03-08 HILLSBORO 113.85 122.20 113.85 125.45 0.98 1.03 1.06 Leading
2016-03-09 HILLSBORO 112.10 122.43 112.10 125.40 NA NA NA NA
2016-02-28 KS PHILLIPS 117.10 123.40 117.10 126.10 1.00 1.00 1.00 Lagging
2016-02-29 KS PHILLIPS 117.80 123.47 117.80 125.75 0.99 1.00 1.00 Lagging
2016-03-01 KS PHILLIPS 117.35 123.54 117.35 126.10 NaN NA NaN Lagging
2016-03-02 KS PHILLIPS 115.25 123.45 115.25 126.55 0.99 0.99 1.00 Lagging
2016-03-03 KS PHILLIPS 116.45 123.78 116.45 126.35 0.99 0.99 1.00 Lagging
2016-03-04 KS PHILLIPS 116.15 122.97 116.15 126.25 0.99 1.00 1.00 Lagging
2016-03-05 KS PHILLIPS 116.15 123.19 116.15 126.05 0.99 1.00 1.00 Lagging
2016-03-06 KS PHILLIPS 117.45 123.22 117.45 125.85 0.99 1.00 1.00 Lagging
2016-03-07 KS PHILLIPS 116.55 123.60 116.55 125.95 0.99 1.00 1.00 Lagging
2016-03-08 KS PHILLIPS 117.05 123.72 117.05 125.80 0.99 0.99 1.00 Lagging
2016-03-09 KS PHILLIPS 117.35 123.32 117.35 125.30 NA NA NA NA
2016-02-28 LYNCHBURG 116.10 123.22 116.10 127.15 0.98 1.00 1.01 Lagging
2016-02-29 LYNCHBURG 117.50 123.56 117.50 127.05 0.98 1.00 1.00 Lagging
2016-03-01 LYNCHBURG 116.90 123.36 116.90 126.80 NaN NA NaN Lagging
2016-03-02 LYNCHBURG 116.60 123.34 116.60 127.05 0.98 1.00 1.00 Lagging
2016-03-03 LYNCHBURG 115.50 123.32 115.50 126.75 0.97 1.00 1.00 Lagging
2016-03-04 LYNCHBURG 116.85 123.38 116.85 127.10 0.98 1.00 1.00 Lagging
2016-03-05 LYNCHBURG 116.40 123.45 116.40 127.35 0.98 1.00 1.00 Lagging
2016-03-06 LYNCHBURG 115.25 123.03 115.25 127.10 0.98 1.00 1.01 Lagging
2016-03-07 LYNCHBURG 117.30 123.43 117.30 127.05 0.98 1.00 1.00 Lagging
2016-03-08 LYNCHBURG 117.10 123.93 117.10 127.20 0.98 1.00 1.00 Lagging
2016-03-09 LYNCHBURG 118.80 124.20 118.80 127.20 NA NA NA NA
2016-02-28 MANCHESTER 161 113.80 122.82 113.80 126.90 NA NA NA NA
2016-02-29 MANCHESTER 161 113.55 122.72 113.55 125.50 NA NA NA NA
2016-03-01 MANCHESTER 161 112.20 122.22 112.20 125.70 NA NA NA NA
2016-03-02 MANCHESTER 161 108.45 121.12 108.45 124.65 NA NA NA NA
2016-03-03 MANCHESTER 161 112.90 121.31 112.90 124.35 NA NA NA NA
2016-03-04 MANCHESTER 161 111.50 121.55 111.50 125.25 NA NA NA NA
2016-03-05 MANCHESTER 161 110.20 122.75 110.20 126.65 NA NA NA NA
2016-03-06 MANCHESTER 161 112.80 122.30 112.80 125.95 NA NA NA NA
2016-03-07 MANCHESTER 161 112.80 122.80 112.80 125.40 NA NA NA NA
2016-03-08 MANCHESTER 161 114.70 122.95 114.70 125.40 NA NA NA NA
2016-03-09 MANCHESTER 161 117.10 122.70 117.10 125.85 NA NA NA NA
2016-02-28 MT PLEASANT 116.60 122.49 116.60 128.20 NA NA NA NA
2016-02-29 MT PLEASANT 116.10 123.17 116.10 127.50 NA NA NA NA
2016-03-01 MT PLEASANT 115.10 122.88 115.10 126.90 NA NA NA NA
2016-03-02 MT PLEASANT 116.35 121.65 116.35 127.05 NA NA NA NA
2016-03-03 MT PLEASANT 115.70 121.76 115.70 126.30 NA NA NA NA
2016-03-04 MT PLEASANT 115.10 121.90 115.10 127.65 NA NA NA NA
2016-03-05 MT PLEASANT 113.45 122.25 113.45 127.95 NA NA NA NA
2016-03-06 MT PLEASANT 116.95 122.55 116.95 127.80 NA NA NA NA
2016-03-07 MT PLEASANT 117.75 123.01 117.75 127.90 NA NA NA NA
2016-03-08 MT PLEASANT 117.25 123.78 117.25 127.75 NA NA NA NA
2016-03-09 MT PLEASANT 117.85 123.96 117.85 128.40 NA NA NA NA
2016-02-28 RALLY HILL 115.45 122.29 115.45 125.55 1.00 1.03 1.08 Leading
2016-02-29 RALLY HILL 116.90 122.71 116.90 125.70 1.00 1.02 1.05 Leading
2016-03-01 RALLY HILL 114.75 122.12 114.75 125.45 1.00 1.03 1.05 Leading
2016-03-02 RALLY HILL 112.40 121.31 112.40 125.25 0.99 1.00 1.03 Leading
2016-03-03 RALLY HILL 113.95 121.44 113.95 124.85 0.99 1.01 1.01 Leading
2016-03-04 RALLY HILL 115.15 121.65 115.15 124.80 1.00 1.01 1.03 Leading
2016-03-05 RALLY HILL 110.20 121.64 110.20 125.15 1.00 1.01 1.06 Leading
2016-03-06 RALLY HILL 110.10 121.93 110.10 124.85 1.00 1.02 1.08 Leading
2016-03-07 RALLY HILL 112.30 122.03 112.30 124.85 1.00 1.01 1.07 Leading
2016-03-08 RALLY HILL 111.45 122.35 111.45 125.50 1.00 1.01 1.06 Leading
2016-03-09 RALLY HILL 110.15 122.35 110.15 124.80 NA NA NA NA
2016-02-28 RED HILL 116.60 123.00 116.60 125.50 0.94 0.99 1.00 Lagging
2016-02-29 RED HILL 117.70 122.84 117.70 124.95 0.93 0.97 0.98 Lagging
2016-03-01 RED HILL 116.55 122.99 116.55 124.75 NaN NA NaN Lagging
2016-03-02 RED HILL 116.55 122.95 116.55 124.70 0.95 0.98 0.98 Lagging
2016-03-03 RED HILL 117.60 123.04 117.60 124.90 0.95 0.98 0.99 Lagging
2016-03-04 RED HILL 117.75 122.99 117.75 124.90 0.94 0.98 0.99 Lagging
2016-03-05 RED HILL 117.45 122.71 117.45 125.35 0.96 0.99 1.00 Lagging
2016-03-06 RED HILL 116.70 123.00 116.70 124.95 0.94 0.98 1.01 Lagging
2016-03-07 RED HILL 118.15 122.60 118.15 124.95 0.94 0.97 0.98 Lagging
2016-03-08 RED HILL 118.10 122.75 118.10 124.75 0.94 0.97 0.98 Lagging
2016-03-09 RED HILL 117.40 122.60 117.40 124.70 NA NA NA NA
2016-02-28 SALEM 115.40 123.26 115.40 126.15 NA NA NA NA
2016-02-29 SALEM 115.25 122.95 115.25 125.45 NA NA NA NA
2016-03-01 SALEM 116.25 122.69 116.25 125.90 NA NA NA NA
2016-03-02 SALEM 112.70 122.58 112.70 126.10 NA NA NA NA
2016-03-03 SALEM 115.05 122.51 115.05 126.15 NA NA NA NA
2016-03-04 SALEM 115.00 122.45 115.00 126.40 NA NA NA NA
2016-03-05 SALEM 114.55 122.66 114.55 126.45 NA NA NA NA
2016-03-06 SALEM 114.25 122.46 114.25 125.85 NA NA NA NA
2016-03-07 SALEM 114.35 123.14 114.35 126.05 NA NA NA NA
2016-03-08 SALEM 113.75 123.19 113.75 126.15 NA NA NA NA
2016-03-09 SALEM 115.30 122.72 115.30 126.10 NA NA NA NA
2016-02-28 SEWANEE 113.10 120.86 113.10 123.15 NA NA NA NA
2016-02-29 SEWANEE 114.30 121.07 114.30 123.50 NA NA NA NA
2016-03-01 SEWANEE 114.70 120.82 114.70 123.30 NA NA NA NA
2016-03-02 SEWANEE 113.85 120.39 113.85 123.15 NA NA NA NA
2016-03-03 SEWANEE 113.75 119.70 113.75 122.75 NA NA NA NA
2016-03-04 SEWANEE 114.20 120.16 114.20 122.95 NA NA NA NA
2016-03-05 SEWANEE 113.70 120.85 113.70 123.25 NA NA NA NA
2016-03-06 SEWANEE 113.90 120.66 113.90 123.35 NA NA NA NA
2016-03-07 SEWANEE 115.25 120.80 115.25 123.40 NA NA NA NA
2016-03-08 SEWANEE 114.95 121.14 114.95 123.15 NA NA NA NA
2016-03-09 SEWANEE 115.55 121.32 115.55 123.30 NA NA NA NA
2016-02-28 SPRING HILL 116.75 123.88 116.75 125.75 1.01 1.02 1.05 Leading
2016-02-29 SPRING HILL 116.55 123.60 116.55 125.70 1.02 1.05 1.06 Leading
2016-03-01 SPRING HILL 116.30 123.32 116.30 125.30 NaN NA NaN Leading
2016-03-02 SPRING HILL 115.85 123.60 115.85 126.10 1.00 1.01 1.05 Leading
2016-03-03 SPRING HILL 115.55 123.74 115.55 125.95 1.00 1.01 1.01 Leading
2016-03-04 SPRING HILL 116.15 123.50 116.15 125.75 1.01 1.01 1.03 Leading
2016-03-05 SPRING HILL 116.85 123.55 116.85 126.40 1.00 1.02 1.05 Leading
2016-03-06 SPRING HILL 117.30 123.58 117.30 125.70 1.00 1.02 1.04 Leading
2016-03-07 SPRING HILL 116.80 123.88 116.80 125.45 1.02 1.03 1.06 Leading
2016-03-08 SPRING HILL 113.25 123.93 113.25 125.35 1.02 1.04 1.06 Leading
2016-03-09 SPRING HILL 116.30 123.30 116.30 125.20 NA NA NA NA
2016-02-28 UNIONVILLE 111.55 119.60 111.55 123.10 1.00 1.02 1.04 Leading
2016-02-29 UNIONVILLE 116.90 122.72 116.90 125.45 1.02 1.04 1.05 Leading
2016-03-01 UNIONVILLE 111.65 119.28 111.65 123.65 NaN NA NaN Leading
2016-03-02 UNIONVILLE 111.55 121.80 111.55 125.90 1.00 1.00 1.03 Leading
2016-03-03 UNIONVILLE 110.90 119.72 110.90 123.90 1.00 1.00 1.01 Leading
2016-03-04 UNIONVILLE 112.90 122.43 112.90 125.15 1.00 1.01 1.02 Leading
2016-03-05 UNIONVILLE 110.85 119.59 110.85 123.90 1.00 1.01 1.04 Leading
2016-03-06 UNIONVILLE 113.05 122.28 113.05 125.50 1.00 1.02 1.04 Leading
2016-03-07 UNIONVILLE 110.50 119.25 110.50 122.15 1.01 1.03 1.07 Leading
2016-03-08 UNIONVILLE 114.95 122.97 114.95 125.35 1.02 1.04 1.06 Leading
2016-03-09 UNIONVILLE 105.05 119.50 105.05 121.60 NA NA NA NA
2016-02-28 WARTRACE 112.00 119.62 112.00 124.00 1.00 1.01 1.06 Leading
2016-02-29 WARTRACE 115.55 122.88 115.55 125.55 1.00 1.01 1.02 Leading
2016-03-01 WARTRACE 109.10 118.95 109.10 123.95 NaN NA NaN Leading
2016-03-02 WARTRACE 112.15 121.81 112.15 126.15 1.00 1.00 1.01 Leading
2016-03-03 WARTRACE 111.35 119.69 111.35 123.80 1.00 1.00 1.00 Leading
2016-03-04 WARTRACE 115.00 122.35 115.00 125.75 1.00 1.00 1.01 Leading
2016-03-05 WARTRACE 111.10 119.62 111.10 124.85 1.00 1.01 1.03 Leading
2016-03-06 WARTRACE 113.45 122.38 113.45 125.60 1.00 1.01 1.03 Leading
2016-03-07 WARTRACE 113.20 119.03 113.20 123.10 1.00 1.03 1.04 Leading
2016-03-08 WARTRACE 114.75 122.95 114.75 125.35 1.01 1.01 1.03 Leading
2016-03-09 WARTRACE 111.75 119.45 111.75 124.90 NA NA NA NA
2016-02-28 WILLIAMSPORT 113.65 123.86 113.65 127.85 NA NA NA NA
2016-02-29 WILLIAMSPORT 115.95 124.14 115.95 127.65 NA NA NA NA
2016-03-01 WILLIAMSPORT 112.80 122.61 112.80 127.55 NA NA NA NA
2016-03-02 WILLIAMSPORT 112.85 121.78 112.85 127.30 NA NA NA NA
2016-03-03 WILLIAMSPORT 114.55 122.76 114.55 126.50 NA NA NA NA
2016-03-04 WILLIAMSPORT 114.60 122.78 114.60 128.20 NA NA NA NA
2016-03-05 WILLIAMSPORT 114.25 123.46 114.25 127.45 NA NA NA NA
2016-03-06 WILLIAMSPORT 115.65 122.81 115.65 126.85 NA NA NA NA
2016-03-07 WILLIAMSPORT 115.80 124.88 115.80 128.15 NA NA NA NA
2016-03-08 WILLIAMSPORT 116.00 124.80 116.00 127.85 NA NA NA NA
2016-03-09 WILLIAMSPORT 116.55 124.89 116.55 126.90 NA NA NA NA

This chart represents the voltage and power factor for all data points collected for 2016-03-02. The chart allows one to see how much of the data falls out of the voltage and power factor defined region (0.98 (left) to 0.99 (right), and 114v to 126v).

We can see we have more points with Lagging power factors outside the required range for efficient power factor values.

NEED 4 or more…

1 Draw comparisons. * YES - Comparisons between substations 2 Identify trends.
* NO - Trends - Nope, need multiple days… or day without VVar day with VVar 3 Engage a wide audience. * Yes, easy to review… 4 Explain a complicated finding.
* YES - which substations behaved and what where their min and max for pf and voltages 5 Clarify a gap between perception and reality.
* MAYBE - If a person looks at the current momentary… status * the daily status might be different 6 Enable the reader to digest large amounts of information. * YES - Power Factor, Voltage, Ranges, inside the comfort zone,

Plot Two

Heatmap of Voltage to help user to see how all substations performed during each 15 minute period during the day. One chart helps to see who is in the red (low) or in the purple (high) voltage ranges.

1 Draw comparisons.
2 Identify trends.
3 Engage a wide audience.
4 Explain a complicated finding.
5 Clarify a gap between perception and reality.
6 Enable the reader to digest large amounts of information.

Plot Three

Description goes here please… Note, I think I have a better plot to put here, this one kinda goes along with Plot_One, it is just another form of the plot.

Total

Reflection

The section explains any important decisions in the analysis and how those decisions affected the analysis.

Voltage

Voltage seems to be normally like distributed by the hour through out the day and by substation for the entire by hour. This seems good since large flucuations in voltage are bad for consumers and commercial businesses.

Power Factor can flucuation from lagging to leading in on substation in a single day. This requires more effort to control the voltage , watts, and vars across the power lines. Can I show that when voltage is tightly controled we have less variability in the power factor??????

TODO: Find a sub with the best range in voltage and look at its power factor. Then compare it’s MW and MVARS to all other substations.

The section reflects on how the analysis was conducted and reports on the struggles and successes throughout the analysis. The section provides at least one idea or question for future work.

The section provides a rich and well-written reflection of * struggles * Date Formatting * Exploring for relationships

  • successes
    • MegaWatt vs MegaVar
    • MegaWatt vs Power Factor
    • Drilling down from Substation to Meter to find the bad meters
  • Lessons learned Electricity is a pain to review.

The section poses ideas or questions for future work. * multiple days would be benficial * months to months * 12 month analysis of trends * seasonal trends * collect more power factor data down to 15min intervals

Equations

Ohm’s Law

We monitor voltage since a utility can change the voltage across the power lines. Keeping the system balanced at low voltages between 114 and 126 helps to improve the efficeny of the power transmission since lower voltages mean less resistance.

I = Current
R = Resistance
V = Voltage

\(V = I*R\)

\(R = \frac{V}{I}\)

\(I = \frac{V}{R}\)

Calculate watts from Voltage

P(kW) = PF × I(A) × V(V) / 1000

Power - kW, kiloWatts
Power Factor
I - Amps
Voltage - Volts

3 Phase
P(kW) = √3 × PF × I(A) × VL-L(V) / 1000

Power Factor

P, Real Power - kW -> PMWD3D (MW) Real power is kilowatts, in the initial dataset this is represented as PMWD3D.

S, Apparent Power
We will be solving for apparent power.

Q, Reactive Power Reactive power is kVAR, in the initial dataset PMQD3D is Delivered and
PMQR3D is Received. The total kVars are (PMQD3D-PMQR3D).

The Power Triangle Equation

See Reference for explanation

\(S^2 = P^2 + Q^2\)

\(S=\sqrt{P^2 + Q^2}\)

Power Factor

The power factor is defined as the ratio of real power to apparent power.

\[Power Factor=\frac{P}{\sqrt{P^2 + Q^2}}\]

\[Power Factor =\frac{kW}{\sqrt{kW^2 + (kVAR Delieverd - kVAR Received)^2}}\]

Leading and Lagging Power Factor

  • Delivered VARs - Received VARs > 0 Lagging
  • Delivered VARs - Received VARs < 0 Leading

References

https://en.wikipedia.org/wiki/Power_factor
https://en.wikipedia.org/wiki/Ohm%27s_law
https://en.wikipedia.org/wiki/Volt-ampere_reactive
http://www.statpower.net/Content/310/R%20Stuff/SampleMarkdown.html
http://rmarkdown.rstudio.com/authoring_basics.html
http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm
http://vita.had.co.nz/papers/tidy-data.pdf http://adv-r.had.co.nz/Style.html

https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf https://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf http://www.r-statistics.com/2012/03/do-more-with-dates-and-times-in-r-with-lubridate-1-1-0/ http://www.statmethods.net/management/aggregate.html http://yihui.name/knitr/options/ http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html http://kbroman.org/knitr_knutshell/pages/Rmarkdown.html http://data.princeton.edu/R/linearModels.html https://statistics.laerd.com/statistical-guides/pearson-correlation-coefficient-statistical-guide.php